sthalles/SimCLR
PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations
This project helps machine learning engineers or researchers pre-train image recognition models without needing large, labeled datasets. It takes a collection of unlabeled images and outputs a neural network that can extract meaningful features from these images. This pre-trained network can then be fine-tuned with a smaller, labeled dataset for specific image classification tasks.
2,480 stars. No commits in the last 6 months.
Use this if you need to train robust image classification models but have limited access to extensively labeled image data.
Not ideal if you are looking for an out-of-the-box solution for immediate image classification without further model training or fine-tuning.
Stars
2,480
Forks
492
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 04, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/sthalles/SimCLR"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Compare
Related frameworks
AdaptiveMotorControlLab/CEBRA
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
theolepage/sslsv
Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker...
PaddlePaddle/PASSL
PASSL包含 SimCLR，MoCo v1/v2，BYOL，CLIP，PixPro，simsiam, SwAV, BEiT，MAE 等图像自监督算法以及 Vision...
YGZWQZD/LAMDA-SSL
30 Semi-Supervised Learning Algorithms
ModSSC/ModSSC
ModSSC: A Modular Framework for Semi Supervised Classification